X is looking to make the practice of law radically more efficient and accurate by developing and launching breakthrough technologies that rely heavily on large language models.
Requirements
- Experience in NLP applied research
- Professional experience in developing and training ML models for product applications
- Proficiency in MLOps, including model deployment, versioning and performance monitoring in production environments
- Experience building pipelines for data processing (e.g. Beam, Flume, Flink, KubeFlow, Spark)
- Experience optimizing system/model performance (e.g. speed, cost, throughput)
- Strong proficiency in Python and standard software engineering practices (testing, designing architectures, version control, etc)
- Experience applying LLMs for large scale document processing, including tasks like annotation, entity extraction, etc.
Responsibilities
- Explore, apply, and innovate state-of-the-art LLM & machine learning techniques to solve real-world problems
- Design and implement ML workflows, such as batch inference, agentic processes, active learning, fine tuning and data labeling
- Design and implement robust, automated, production-level software using horizontally scalable components
- Work on prompt optimization and evaluation, pipeline implementation and scalability, and production monitoring and performance
- Fine-tune SOTA models with domain-specific knowledge to improve reasoning performance
- Apply knowledge and expertise to come up with and implement novel and impactful ideas
- Innovate ways to improve model accuracy and quantify drift, overfitting and regression
Other
- Work effectively with cross-functional teams of engineers, product managers, and domain experts
- Provide direction and focus in areas of high ambiguity
- Excellent written and verbal communication skills
- Experience working in start-up like environments
